LAUSR

A sliding mode control strategy is presented based on the online sequential extreme learning machine (OS-ELM) environmental parameter identifier. The aim is to eliminate the effects of uncertain environmental parameters in the lane-changing control of the intelligent vehicle platoon. Firstly, the vehicle platoon dynamics model is established considering the communication topology between the vehicle platoon. Then, the vehicle platoon distance error model is obtained based on the constant time distance strategy. Moreover, a sliding mode control strategy based on OS-ELM environmental parameter identifier is proposed to deal with the uncertainty of some environment parameters. Furthermore, finite-time stability and string stability of the system are analyzed by constructing a Lyapunov function. Finally, Carsim/Simulink simulations further verify the effectiveness of the method in this research. The results show that the proposed method estimates the uncertainty parameters accurately in this vehicle platoon. Meanwhile, it guarantees the string stability, and realizes the rapid convergence of errors.